A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty

Perfect knowledge of the underlying state transition probabilities is necessary for designing an optimal intervention strategy for a given Markovian genetic regulatory network. However, in many practical situations, the complex nature of the network and/or identification costs limit the availability of such perfect knowledge. To address this difficulty, we propose to take a Bayesian approach and represent the system of interest as an uncertainty class of several models, each assigned some probability, which reflects our prior knowledge about the system. We define the objective function to be the expected cost relative to the probability distribution over the uncertainty class and formulate an optimal Bayesian robust intervention policy minimizing this cost function. The resulting policy may not be optimal for a fixed element within the uncertainty class, but it is optimal when averaged across the uncertainly class. Furthermore, starting from a prior probability distribution over the uncertainty class and collecting samples from the process over time, one can update the prior distribution to a posterior and find the corresponding optimal Bayesian robust policy relative to the posterior distribution. Therefore, the optimal intervention policy is essentially nonstationary and adaptive.     

Giving a scientific basis for uncertainty factors used in global life cycle inventory databases: an algorithm to update factors using new information

Purpose

Life cycle inventory (LCI) databases provide generic data on exchange values associated with unit processes. The “ecoinvent” LCI database estimates the uncertainty of all exchange values through the application of the so-called pedigree approach. In the first release of the database, the used uncertainty factors were based on experts’ judgments. In 2013, Ciroth et al. derived empirically based factors. These, however, assumed that the same uncertainty factors could be used for all industrial sectors and fell short of providing basic uncertainty factors. The work presented here aims to overcome these limitations.

Methods

The proposed methodological framework is based on the assessment of more than 60 data sources (23,200 data points) and the use of Bayesian inference. Using Bayesian inference allows an update of uncertainty factors by systematically combining experts’ judgments and other information we already have about the uncertainty factors with new data.

Results and discussion

The implementation of the methodology over the data sources results in the definition of new uncertainty factors for all additional uncertainty indicators and for some specific industrial sectors. It also results in the definition of some basic uncertainty factors. In general, the factors obtained are higher than the ones obtained in previous work, which suggests that the experts had initially underestimated uncertainty. Furthermore, the presented methodology can be applied to update uncertainty factors as new data become available.

Conclusions

In practice, these uncertainty factors can systematically be incorporated in LCI databases as estimates of exchange value uncertainty where more formal uncertainty information is not available. The use of Bayesian inference is applied here to update uncertainty factors but can also be used in other life cycle assessment developments in order to improve experts’ judgments or to update parameter values when new data can be accessed.

     

Public Reactions to Risk Messages Communicating Different Sources of Uncertainty: An Experimental Test

There is an abundant literature on the challenge of integrating uncertainties in experts’ risk assessments, but the evidence on the way they are understood by the public is scarce and mixed. This study aims to better understand the effect of communicating different sources of uncertainty in risk communication. A causal design was employed to test the effect of communicating risk messages varying in type of advisory warning (no risk and suggests no protective measure, or risk and recommends a protective measure) and sources of uncertainty (no uncertainty, divergence between experts, contradictory data, or lack of data) on public reactions. Participants from the general public (N = 434) were randomly assigned to read and react to variants of a fictitious government message discussing the presence of a new micro-organism found in tap water. Multiple analysis of variance showed that to report uncertainty from divergence between experts or from contradictory data reduced the adherence to the message, but not to mention the lack of data. Moreover, the communication of diverse sources of uncertainty did not affect trust in the government when the advisory warning stated there was a risk and recommended a protective measure. These findings have important implications for risk communication.     

Evaluating extreme risks in invasion ecology: learning from banking compliance

Increasing international trade has exacerbated the risks of ecological damage due to invasive pests and diseases. For extreme events such as invasions of damaging exotic species or natural catastrophes, there are no or very few directly relevant data, so expert opinion must be relied on heavily. Expert opinion must be as fully informed and calibrated as possible – by available data, by other experts, and by the reasoned opinions of stakeholders. We survey a number of quantitative and non-quantitative methods that have shown promise for improving extreme risk analysis, particularly for assessing the risks of invasive pests and pathogens associated with international trade. We describe the legally inspired regulatory regime for banks, where these methods have been brought to bear on extreme 'operational risks'. We argue that an 'advocacy model' similar to that used in the Basel II compliance regime for bank operational risks and to a lesser extent in biosecurity import risk analyses is ideal for permitting the diversity of relevant evidence about invasive species to be presented and soundly evaluated. We recommend that the process be enhanced in ways that enable invasion ecology to make more explicit use of the methods found successful in banking.     

Do short courses in evidence based medicine improve knowledge and skills? Validation of Berlin questionnaire and before and after study of courses in evidence based medicine

ObjectiveTo develop and validate an instrument for measuring knowledge and skills in evidence based medicine and to investigate whether short courses in evidence based medicine lead to a meaningful increase in knowledge and skills.DesignDevelopment and validation of an assessment instrument and before and after study.SettingVarious postgraduate short courses in evidence based medicine in Germany.ParticipantsThe instrument was validated with experts in evidence based medicine, postgraduate doctors, and medical students. The effect of courses was assessed by postgraduate doctors from medical and surgical backgrounds.InterventionIntensive 3 day courses in evidence based medicine delivered through tutor facilitated small groups.ResultsThe questionnaire distinguished reliably between groups with different expertise in evidence based medicine. Experts attained a threefold higher average score than students. Postgraduates who had not attended a course performed better than students but significantly worse than experts. Knowledge and skills in evidence based medicine increased after the course by 57% (mean score before course 6.3 (SD 2.9) v 9.9 (SD 2.8), P<0.001). No difference was found among experts or students in absence of an intervention.ConclusionsThe instrument reliably assessed knowledge and skills in evidence based medicine. An intensive 3 day course in evidence based medicine led to a significant increase in knowledge and skills.

What is already known on this topic

Numerous observational studies have investigated the impact of teaching evidence based medicine to healthcare professionals, with conflicting resultsMost of the studies were of poor methodological quality

What this study adds

An instrument assessing basic knowledge and skills required for practising evidence based medicine was developed and validatedAn intensive 3 day course on evidence based medicine for doctors from various backgrounds and training level led to a clinically meaningful improvement of knowledge and skills     

To perceive is to doubt: The relativity of perception

The uncertainty or entropy theory of perception is founded on the premise that for perception to occur, there must first of all be uncertainty. That is, perception or awareness is relative to the expectation of the perceiver. This view of perception leads to a seeming-paradox. How can there be uncertainty unless the alternatives have previously been perceived? But, by the premise of the theory, how can the alternatives have been perceived unless there was prior uncertainty? It is shown that this paradox may result physiologically in the concurrence of sensory and motor (or “active”) events during the process of perceiving. It is shown, further, that a close analogy exists between systems of formal logic and systems which perceive through uncertainty. This, in turn, suggests a basis for a calculus of perception.     

Conceptual graph operations for formal visual reasoning in the medical domain

ObjectiveConceptual graphs (CGs) are used to represent clinical guidelines because they support visual reasoning with a logical background, making them a potentially valuable representation for guidelines.Materials and methodsConceptual graph formalism has an essential and basic component: a formal vocabulary that drives all of the other mechanisms, notably specialization and projection. The graph's theoretical operations, such as projection, rules, derivation, constraints, probabilities and uncertainty, support diagrammatic reasoning.ResultsA conceptual graph's graphical user interface includes a multilingual vocabulary management, some query and decision-making facilities and visual graph representations that are simple and interesting for user interactions. The described proposition using the Conceptual Graph user interface (CoGui) improves the performance of the actors in the diagnostic context of heart failure with preserved ejection fraction.DiscussionCGs capture the essential features of the medical processes underlying clinical reasoning. CGs are indeed useful as a way for the physician to represent guidelines, and well-defined semantic representations allow users to have a maximal understanding of the knowledge reasoning process.ConclusionCG operations of visual representations that uncover some of the actual complexities of clinicians’ reasoning have been tested in clinical guideline comprehension and used to translate text and diagrammatic guidelines into computer interpretable representations.     

Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities

Consider case control analysis with a dichotomous exposure variable that is subject to misclassification. If the classification probabilities are known, then methods are available to adjust odds-ratio estimates in light of the misclassification. We study the realistic scenario where reasonable guesses, but not exact values, are available for the classification probabilities. If the analysis proceeds by simply treating the guesses as exact, then even small discrepancies between the guesses and the actual probabilities can seriously degrade odds-ratio estimates. We show that this problem is mitigated by a Bayes analysis that incorporates uncertainty about the classification probabilities as prior information.     

Sequence of events following adoption of contraception: An exploratory analysis of 1973 fertility history data

Abstract

Fertility histories from the 1973 United States National Survey of Family Growth are analyzed in the context of a model of contraceptive use based on a Semi‐Markov processes. This model provides a means of constructing data‐based estimates of probabilities of pregnancy following initial acceptance of a contraceptive method. The algorithm used to construct these estimates recognizes multiple intervals of contraceptive used prior to the events: pregnancy, marital dissolution, or sterilization.

Estimated probabilities of the events marital dissolution and pregnancy for women seeking to delay pregnancy are presented, as are probabilities of contraceptive sterilization for women seeking to prevent subsequent pregnancy. These estimates are compared to one‐step transition probabilities and directly observed NSFG data on pregnancy, marital dissolution, or contraceptive sterilization in an attempt to judge the validity of the model and to assess biases which may result from its use.     

A flavour of Alzheimer's

This article describes how today in the United States neurologists diagnose forms of dementia, such as Alzheimer's disease and frontotemporal dementia. Taking as a starting‐point the pervasive context of uncertainty in the diagnosis of neurodegenerative diseases, it examines how uncertainty is not merely an epistemological obstacle to the making of knowledge. On the contrary, the article analyses how uncertainty positively incites the use of clinicians’ ‘feelings’ in diagnostic work. Drawing on observations of clinical consultations and team meetings, it studies how, alongside contemporary instruments of objectification, clinicians use, share, and discuss their ‘feelings’ to ultimately renew knowledge about brain diseases. In documenting the manner in which medical expertise is bound to a concrete experience of the world, this article further explores how experts’ ‘intuition’ can be grasped as a conscious and effortful process, rather than as something ineffable, resisting analysis, and confined to an unconscious background.     

Beyond protocols: improving the reliability of expert-based risk analysis underpinning invasive species policies

Risk assessment tools for listing invasive alien species need to incorporate all available evidence and expertise. Beyond the wealth of protocols developed to date, we argue that the current way of performing risk analysis has several shortcomings. In particular, lack of data on ecological impacts, transparency and repeatability of assessments as well as the incorporation of uncertainty should all be explicitly considered. We recommend improved quality control of risk assessments through formalized peer review with clear feedback between assessors and reviewers. Alternatively, a consensus building process can be applied to better capture opinions of different experts, thereby maximizing the evidential basis. Elaborating on manageability of invasive species is further needed to fully answer all risk analysis requirements. Tackling the issue of invasive species urges better handling of the acquired information on risk and the exploration of improved methods for decision making on biodiversity management. This is crucial for efficient conservation resource allocation and uptake by stakeholders and the public.     

Adequacy Conditions for Reporting Uncertainty in Chemical Risk Assessments

Uncertainty may influence decision-making. A prerequisite for a decision to be well founded is thus that scientific experts inform decision-makers about all decision relevant uncertainty. A set of conditions is provided for adequate characterization of scientific uncertainty for the purposes of regulatory decision-making. These conditions require specification of (1) the character and degree of uncertainty about the assessment variables, (2) the possibility of reducing the uncertainty, and (3) the degree of agreement among experts. Furthermore, it is required that (4) the information covered by the previous conditions is presented in a clear and comprehensible way. The point of departure is that characterizing scientific uncertainty conceptually means specifying all potentially important possibilities that are consistent with the state of scientific knowledge. The conditions are intended to be applied to human health risk assessment of chemicals. However, the basic approach, to consider potentially important possibilities, should be useful also to environmental, and site-specific risk assessment.     

Quantifying Uncertainty in Predictions of Invasiveness

Using the Australian weed risk assessment (WRA) model as an example, we applied a combination of bootstrapping and Bayesian techniques as a means for explicitly estimating the posterior probability of weediness as a function of an import risk assessment model screening score. Our approach provides estimates of uncertainty around model predictions, after correcting for verification bias arising from the original training dataset having a higher proportion of weed species than would be the norm, and incorporates uncertainty in current knowledge of the prior (base-rate) probability of weediness. The results confirm the high sensitivity of the posterior probability of weediness to the base-rate probability of weediness of plants proposed for importation, and demonstrate how uncertainty in this base-rate probability manifests itself in uncertainty surrounding predicted probabilities of weediness. This quantitative estimate of the weediness probability posed by taxa classified using the WRA model, including estimates of uncertainty around this probability for a given WRA score, would enable bio-economic modelling to contribute to the decision process, should this avenue be pursued. Regardless of whether or not this avenue is explored, the explicit estimates of uncertainty around weed classifications will enable managers to make better informed decisions regarding risk. When viewed in terms of likelihood of weed introduction, the current WRA model outcomes of ‘accept’, ‘further evaluate’, or ‘reject’, whilst not always accurate in terms of weed classification, appear consistent with a high expected cost of mistakenly introducing a weed. The methods presented have wider application to the quantitative prediction of invasive species for situations where the base-rate probability of invasiveness is subject to uncertainty, and the accuracy of the screening test imperfect     

Effects of branch length uncertainty on Bayesian posterior probabilities for phylogenetic hypotheses

In Bayesian phylogenetics, confidence in evolutionary relationships is expressed as posterior probability--the probability that a tree or clade is true given the data, evolutionary model, and prior assumptions about model parameters. Model parameters, such as branch lengths, are never known in advance; Bayesian methods incorporate this uncertainty by integrating over a range of plausible values given an assumed prior probability distribution for each parameter. Little is known about the effects of integrating over branch length uncertainty on posterior probabilities when different priors are assumed. Here, we show that integrating over uncertainty using a wide range of typical prior assumptions strongly affects posterior probabilities, causing them to deviate from those that would be inferred if branch lengths were known in advance; only when there is no uncertainty to integrate over does the average posterior probability of a group of trees accurately predict the proportion of correct trees in the group. The pattern of branch lengths on the true tree determines whether integrating over uncertainty pushes posterior probabilities upward or downward. The magnitude of the effect depends on the specific prior distributions used and the length of the sequences analyzed. Under realistic conditions, however, even extraordinarily long sequences are not enough to prevent frequent inference of incorrect clades with strong support. We found that across a range of conditions, diffuse priors--either flat or exponential distributions with moderate to large means--provide more reliable inferences than small-mean exponential priors. An empirical Bayes approach that fixes branch lengths at their maximum likelihood estimates yields posterior probabilities that more closely match those that would be inferred if the true branch lengths were known in advance and reduces the rate of strongly supported false inferences compared with fully Bayesian integration.     

Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating.     

Knowledge of,and Attitudes Towards Health-related Biotechnology Applications Amongst Australian Year 10 High School Students

Modern biotechnology has a large and rapidly increasing impact on society. New advances in genetics, stem cells and other areas hold great potential for human health but also presenting socioscientific issues that commonly divide public opinion. While knowledge is necessary to develop informed opinions about biotechnology, they may also be influenced by polarized discourse and fiction in the media. Here, we examined prior knowledge about and attitudes towards health-related biotechnological applications in Year 10 high school students from Western Australia using online questionnaires. The impact of teaching on students’ understanding was tested by repeating the questionnaire after a lesson. Finally, students’ argumentation skills were examined by recording responses to statements about biotechnological applications. We found that, prior to instruction, most students exhibited a reasonable understanding of biotechnology. There was little evidence for alternative conceptions, and instruction led to a diversification in understanding. Attitudes towards biotechnology were generally positive but varied. Despite interest in biotechnological issues, argument for positions was generally cognitive-affective in nature. Consequently, biotechnology is a relevant topic for science education, and presents excellent opportunities to build on pre-existing knowledge. Rather than expanding students’ knowledge, our results suggest educators should focus on deepening existing understanding and strengthening argumentation skills.     

Quantifying uncertainty in predictions of invasiveness, with emphasis on weed risk assessment

Using the Australian Weed Risk Assessment (WRA) model as an example, we applied a combination of bootstrapping and Bayesian techniques as a means of explicitly estimating the posterior probability of weediness as a function of an import risk assessment model screening score. Our approach provides estimates of uncertainty around model predictions, after correcting for verification bias arising from the original training dataset having a higher proportion of weed species than would be the norm, and incorporates uncertainty in current knowledge of the prior (base-rate) probability of weediness. The results confirm the high sensitivity of the posterior probability of weediness to the base-rate probability of weediness of plants proposed for importation, and demonstrate how uncertainty in this base-rate probability manifests itself in uncertainty surrounding predicted probabilities of weediness. This quantitative estimate of the weediness probability posed by taxa classified using the WRA model, including estimates of uncertainty around this probability for a given WRA score, would enable bio-economic modelling to contribute to the decision process, should this avenue be pursued. Regardless of whether or not this avenue is explored, the explicit estimates of uncertainty around weed classifications will enable managers to make better informed decisions regarding risk. When viewed in terms of likelihood of weed introduction, the current WRA model outcomes of ‘accept’, ‘further evaluate’ or ‘reject’, whilst not always accurate in terms of weed classification, appear consistent with a high-expected cost of mistakenly introducing a weed. The methods presented have wider application to the quantitative prediction of invasive species for situations where the base-rate probability of invasiveness is subject to uncertainty, and the accuracy of the screening test imperfect.     

A fish-based index of estuarine ecological quality incorporating information from both scientific fish survey and experts knowledge

In the Water Framework Directive (European Union) context, a multimetric fish based index is required to assess the ecological status of French estuarine water bodies. A first indicator called ELFI was developed, however similarly to most indicators, the method to combine the core metrics was rather subjective and this indicator does not provide uncertainty assessment. Recently, a Bayesian method to build indicators was developed and appeared relevant to select metrics sensitive to global anthropogenic pressure, to combine them objectively in an index and to provide a measure of uncertainty around the diagnostic. Moreover, the Bayesian framework is especially well adapted to integrate knowledge and information not included in surveys data. In this context, the present study used this Bayesian method to build a multimetric fish based index of ecological quality accounting for experts knowledge. The first step consisted in elaborating a questionnaire to collect assessments from different experts then in building relevant priors to summarize those assessments for each water body. Then, these priors were combined with surveys data in the index to complement the diagnosis of quality. Finally, a comparison between diagnoses using only fish data and using both information sources underlined experts knowledge contribution. Regarding the results, 68% of the diagnosis matched demonstrating that including experts knowledge thanks to the Bayesian framework confirmed or slightly modified the diagnosis provided by survey data but influenced uncertainty around the diagnostic and appeared especially relevant in terms of risk management.     

Risk analysis of the invasion pathway of the Asian gypsy moth: a known forest invader

Risk is defined with many minor variations in the biological literature. Common to most definitions are the following elements: the probability of a future event; and the consequences of the event, usually with respect to some predefined human value. Risk analysis includes elements of risk assessment (quantification of risk), uncertainty (of the event and its consequences), risk management (reducing risk to an acceptable level), and development of policy to balance finite resources with uncertainty and risk tolerance. When biological invasion and its risk are jointly examined, it is common that the consequences of invasion are not explicitly quantified, but understood to be sufficiently negative that it must be minimized to the extent possible. Risk analysis then becomes quantification of the probabilities of an introduction (event) and that the introduction leads to establishment, and the uncertainty of those probabilities. I describe a risk analysis framework for the Asian gypsy moth—a known invader—in its pathway. The framework uses the available information regarding the transportation route of the vector (ships), and a phenology model that estimates vector contamination (propagule size), the probability of introduction, and the probability of initial establishment given an introduction. Reducing propagule pressure is arguably the most important factor in reducing biological invasion; propagule pressure can be reduced by inspection and sanitation of the pathway vector (e.g., ships, trucks, humans) at the point(s) of departure and at the point of entry. I demonstrate how the risk analysis framework can be used to more efficiently target incoming ships for inspection and propagule pressure reduction.     

Quantifying evidence for candidate gene polymorphisms: Bayesian analysis combining sequence-specific and quantitative trait loci colocation information

We calculate posterior probabilities for candidate genes as a function of genomic location. Posterior probabilities for quantitative trait loci (QTL) presence in a small interval are calculated using a Bayesian model-selection approach based on the Bayesian information criterion (BIC) and used to combine QTL colocation information with sequence-specific evidence, e.g., from differential expression and/or association studies. Our method takes into account uncertainty in estimation of number and locations of QTL and estimated map position. Posterior probabilities for QTL presence were calculated for simulated data with n = 100, 300, and 1200 QTL progeny and compared with interval mapping and composite-interval mapping. Candidate genes that mapped to QTL regions had substantially larger posterior probabilities. Among candidates with a given Bayes factor, those that map near a QTL are more promising for further investigation with association studies and functional testing or for use in marker-aided selection. The BIC is shown to correspond very closely to Bayes factors for linear models with a nearly noninformative Zellner prior for the simulated QTL data with n > or = 100. It is shown how to modify the BIC to use a subjective prior for the QTL effects.